Femtosecond Electron Diffraction Reveals Local Disorder and Local Anharmonicity in Thermoelectric SnSe.

In addition to long-range periodicity, local disorder, with local structures deviating from the average lattice structure, dominates the physical properties of phonons, electrons, and spin subsystems in crystalline functional materials. Experimentally characterizing the 3D atomic configuration of such a local disorder and correlating it with advanced functions remains challenging. Using a combination of femtosecond electron diffraction, structure factor calculations, and time-dependent density functional theory molecular dynamics simulations, the static local disorder and its local anharmonicity in thermoelectric SnSe are identified exclusively. The ultrafast structural dynamics reveal that the crystalline SnSe is composed of multiple locally correlated configurations dominated by the static off-symmetry displacements of Sn (≈0.4 Å) and such a set of locally correlated structures is termed local disorder. Moreover, the anharmonicity of this local disorder induces an ultrafast atomic displacement within 100 fs, indicating the signature of probable THz Einstein oscillators. The identified local disorder and local anharmonicity suggest a glass-like thermal transport channel, which updates the fundamental insight into the long-debated ultralow thermal conductivity of SnSe. The method of revealing the 3D local disorder and the locally correlated interactions by ultrafast structural dynamics will inspire broad interest in the construction of structure-property relationships in material science.

Entropy SVM-Based Recognition of Transient Surges in HVDC Transmissions.

Protection based on transient information is the primary protection of high voltage direct current (HVDC) transmission systems. As a major part of protection function, accurate identification of transient surges is quite crucial to ensure the performance and accuracy of protection algorithms. Recognition of transient surges in an HVDC system faces two challenges: signal distortion and small number of samples. Entropy, which is stable in representing frequency distribution features, and support vector machine (SVM), which is good at dealing with samples with limited numbers, are adopted and combined in this paper to solve the transient recognition problems. Three commonly detected transient surges-single-pole-to-ground fault (GF), lightning fault (LF), and lightning disturbance (LD)-are simulated in various scenarios and recognized with the proposed method. The proposed method is proved to be effective in both feature extraction and type classification and shows great potential in protection applications.